Hundreds of thousands of people suffer from neurological injuries and disease, resulting in the permanent loss of motor function. Effective treatments do not currently exist for many of these conditions. Neural prosthetic systems have filled several of these treatment gaps, but many still remain. One promising class of neural prosthetic system aims to provide direct brain control of prosthetic arms. These motor prostheses translate neural activity from the brain into control signals to guide the prosthetic arm, working through a so-called "decode algorithm". We believe that the time is right to investigate how a potentially high-performance and high-robustness brain control signal (human ECoG) can work through a new class of decode algorithm, and control a state-of-the-art high degree of freedom prosthetic arm (DARPA arm). To investigate this possibility, with the aim of demonstrating very high performance and high robustness control of a high dexterity arm within two years, our Specific Aims (SAs) are as follows. SA1: we will seek to demonstrate modulation of human ECoG signals during a systematic set of reaching, grasping, and finger movement tasks. SA2: we will use human ECoG signals for the closed-loop control of discrete, sequential joint movements. SA3: we will use human ECoG signals for closed-loop, smooth, high-speed control of the arm, hand, and ultimately even individuated finger movements. PUBLIC HEALTH RELEVANCE: Hundreds of thousands of people in the US alone suffer from neurological injuries and disease, resulting in the permanent loss of motor function or even the ability to communicate. Conditions include upper spinal cord injury, ALS, and amputation. Our proposed research aims to dramatically increase the performance of neurally-controlled (ECoG controlled) prosthetic devices, specifically the recently designed DARPA Arm (from the Applied Physics Lab), with the goal of improving the quality of life for these patients.